% Key papers for Natural gradient and Fisher approximations (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2019_a_29128,
  title = {{A Variational Inequality Perspective on Generative Adversarial Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1129},
}

@inproceedings{icml2016_low_35897,
  title = {{Low-rank tensor completion: a Riemannian manifold preconditioning approach}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/kasai16.html},
}

@inproceedings{neurips2018_fast_57125,
  title = {{Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11908},
}

@inproceedings{iclr2017_distributed_28325,
  title = {{Distributed Second-Order Optimization using Kronecker-Factored Approximations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=SkkTMpjex},
}

@inproceedings{neurips2020_practical_60599,
  title = {{Practical Quasi-Newton Methods for Training Deep Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16903},
}

@inproceedings{iclr2018_kronecker_28558,
  title = {{Kronecker-factored Curvature Approximations for Recurrent Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/12},
}

@inproceedings{neurips2016_provable_55946,
  title = {{Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7390},
}

@inproceedings{neurips2019_limitations_58547,
  title = {{Limitations of the empirical Fisher approximation for natural gradient descent}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13574},
}

@inproceedings{neurips2018_exact_57109,
  title = {{Exact natural gradient in deep linear networks and its application to the nonlinear case}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11577},
}

@inproceedings{icml2016_a_35717,
  title = {{A Kronecker-factored approximate Fisher matrix for convolution layers}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/grosse16.html},
}

@inproceedings{neurips2017_diving_56262,
  title = {{Diving into the shallows: a computational perspective on large-scale shallow learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9160},
}

@inproceedings{neurips2016_learning_55832,
  title = {{Learning a Metric Embedding  for Face Recognition using the Multibatch Method}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6958},
}

@inproceedings{icml2017_relative_36366,
  title = {{Relative Fisher Information and Natural Gradient for Learning Large Modular Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/458},
}

@inproceedings{neurips2020_understanding_61034,
  title = {{Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17192},
}

@inproceedings{neurips2021_efficient_61901,
  title = {{Efficient Neural Network Training via Forward and Backward Propagation Sparsification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27539},
}

@inproceedings{icml2017_approximate_36069,
  title = {{Approximate Steepest Coordinate Descent}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/723},
}

@inproceedings{neurips2016_improved_55781,
  title = {{Improved Dropout for Shallow and Deep Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/8492},
}

@inproceedings{icml2019_natural_37564,
  title = {{Natural Analysts in Adaptive Data Analysis}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3916},
}

@inproceedings{iclr2020_kernelized_29522,
  title = {{Kernelized Wasserstein Natural Gradient}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=Hklz71rYvS},
}

@inproceedings{neurips2022_nonlinear_65048,
  title = {{Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54016},
}

@inproceedings{neurips2024_an_71714,
  title = {{An Improved Empirical Fisher Approximation for Natural Gradient Descent}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95572},
}

@inproceedings{neurips2025_improving_75220,
  title = {{Improving Energy Natural Gradient Descent through Woodbury, Momentum, and Randomization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=5YMZfufpfY},
}

@inproceedings{icml2024_revisiting_45802,
  title = {{Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32650},
}

@inproceedings{neurips2024_a_73836,
  title = {{A Layer-Wise Natural Gradient Optimizer for Training Deep Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93672},
}

@inproceedings{aaai2025_the_10976,
  title = {{The Gradient of Algebraic Model Counting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34132},
}

@inproceedings{iclr2024_fosi_34312,
  title = {{FOSI: Hybrid First and Second Order Optimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18763},
}

@inproceedings{icml2024_on_45686,
  title = {{On the Hardness of Probabilistic Neurosymbolic Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32766},
}

@inproceedings{icml2024_on_45813,
  title = {{On a Neural Implementation of Brenier's Polar Factorization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32639},
}

@inproceedings{icml2025_sassha_46244,
  title = {{Sassha: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46316},
}

@inproceedings{icml2025_fishers_48442,
  title = {{Fishers for Free? Approximating the Fisher Information Matrix by Recycling the Squared Gradient Accumulator}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44175},
}

@inproceedings{icml2025_learning_49014,
  title = {{Learning Mixtures of Experts with EM: A Mirror Descent Perspective}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43609},
}

@inproceedings{icml2026_randomized_54391,
  title = {{Randomized Advantage Transformation (RAT): Computing Natural Policy Gradients via Direct Backpropagation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=oVtiySvpXz},
}

@inproceedings{neurips2024_approximated_74524,
  title = {{Approximated Orthogonal Projection Unit: Stabilizing Regression Network Training Using Natural Gradient}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93058},
}

@inproceedings{neurips2025_stable_76914,
  title = {{Stable Coresets via Posterior Sampling: Aligning Induced and Full Loss Landscapes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=OOQMahom6P},
}

@inproceedings{neurips2025_koala_77047,
  title = {{KOALA++: Efficient Kalman-Based Optimization with Gradient-Covariance Products}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Pvoy6fWaRJ},
}

@inproceedings{neurips2026_layerwise_90489,
  title = {{Layerwise LQR for Geometry-Aware Optimization of Deep Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/148444},
}

@inproceedings{neurips2026_em_91867,
  title = {{EM-NeSy: Expectation Maximization for Neurosymbolic Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/149935},
}

@inproceedings{icml2024_improving_44131,
  title = {{Improving Computational Complexity in Statistical Models with Local Curvature Information}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34321},
}

@inproceedings{icml2024_structured_44662,
  title = {{Structured Inverse-Free Natural Gradient Descent: Memory-Efficient \& Numerically-Stable KFAC}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33790},
}

@inproceedings{icml2025_near_47855,
  title = {{Near-optimal Sketchy Natural Gradients for Physics-Informed Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44747},
}